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Multivariate profiling of metabolites in human disease: Method evaluation and application to prostate cancer
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Computational Life Science Cluster (CLiC))
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

There is an ever increasing need of new technologies for identification of molecular markers for early diagnosis of fatal diseases to allow efficient treatment. In addition, there is great value in finding patterns of metabolites, proteins or genes altered in relation to specific disease conditions to gain a deeper understanding of the underlying mechanisms of disease development. If successful, scientific achievements in this field could apart from early diagnosis lead to development of new drugs, treatments or preventions for many serious diseases.  Metabolites are low molecular weight compounds involved in the chemical reactions taking place in the cells of living organisms to uphold life, i.e. metabolism. The research field of metabolomics investigates the relationship between metabolite alterations and biochemical mechanisms, e.g. disease processes. To understand these associations hundreds of metabolites present in a sample are quantified using sensitive bioanalytical techniques. In this way a unique chemical fingerprint is obtained for each sample, providing an instant picture of the current state of the studied system. This fingerprint or picture can then be utilized for the discovery of biomarkers or biomarker patterns of biological and clinical relevance.

In this thesis the focus is set on evaluation and application of strategies for studying metabolic alterations in human tissues associated with disease. A chemometric methodology for processing and modeling of gas chromatography-mass spectrometry (GC-MS) based metabolomics data, is designed for developing predictive systems for generation of representative data, validation and result verification, diagnosis and screening of large sample sets.

The developed strategies were specifically applied for identification of metabolite markers and metabolic pathways associated with prostate cancer disease progression. The long-term goal was to detect new sensitive diagnostic/prognostic markers, which ultimately could be used to differentiate between indolent and aggressive tumors at diagnosis and thus aid in the development of personalized treatments. Our main finding so far is the detection of high levels of cholesterol in prostate cancer bone metastases. This in combination with previously presented results suggests cholesterol as a potentially interesting therapeutic target for advanced prostate cancer. Furthermore we detected metabolic alterations in plasma associated with metastasis development. These results were further explored in prospective samples attempting to verify some of the identified metabolites as potential prognostic markers.

Place, publisher, year, edition, pages
Umeå: Umeå Universitet , 2012. , 43 p.
Keyword [en]
metabolite profiling, metabolomics, predictive metabolomics, mass spectrometry, GC-MS, biomarkers, chemometrics, design of experiments, multivariate data analysis, prostate cancer, bone metastases, plasma
National Category
Other Medical Sciences not elsewhere specified
Research subject
biological chemistry
Identifiers
URN: urn:nbn:se:umu:diva-50968ISBN: 978-91-7459-344-0 (print)OAI: oai:DiVA.org:umu-50968DiVA: diva2:471658
Public defence
2012-01-27, KBC-huset, KB3B1, Umeå universitet, Umeå, 10:00 (Swedish)
Opponent
Supervisors
Available from: 2012-01-04 Created: 2012-01-02 Last updated: 2012-01-11Bibliographically approved
List of papers
1. Predictive metabolite profiling applying hierarchical multivariate curve resolution to GC-MS data: a potential tool for multi-parametric diagnosis
Open this publication in new window or tab >>Predictive metabolite profiling applying hierarchical multivariate curve resolution to GC-MS data: a potential tool for multi-parametric diagnosis
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2006 (English)In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 5, no 6, 1407-1414 p.Article in journal (Refereed) Published
Abstract [en]

A method for predictive metabolite profiling based on resolution of GC-MS data followed by multivariate data analysis is presented and applied to three different biofluid data sets (rat urine, aspen leaf extracts, and human blood plasma). Hierarchical multivariate curve resolution (H-MCR) was used to simultaneously resolve the GC-MS data into pure profiles, describing the relative metabolite concentrations between samples, for multivariate analysis. Here, we present an extension of the H-MCR method allowing treatment of independent samples according to processing parameters estimated from a set of training samples. Predictions or inclusion of the new samples, based on their metabolite profiles, into an existing model could then be carried out, which is a requirement for a working application within, e.g., clinical diagnosis. Apart from allowing treatment and prediction of independent samples the proposed method also reduces the time for the curve resolution process since only a subset of representative samples have to be processed while the remaining samples can be treated according to the obtained processing parameters. The time required for resolving the 30 training samples in the rat urine example was approximately 13 h, while the treatment of the 30 test samples according to the training parameters required only approximately 30 s per sample (approximately 15 min in total). In addition, the presented results show that the suggested approach works for describing metabolic changes in different biofluids, indicating that this is a general approach for high-throughput predictive metabolite profiling, which could have important applications in areas such as plant functional genomics, drug toxicity, treatment efficacy and early disease diagnosis.

Place, publisher, year, edition, pages
American Chemical Society, 2006
Keyword
Animals, Blood Proteins/*analysis, Data Interpretation; Statistical, Gas Chromatography-Mass Spectrometry, Humans, Laboratory Techniques and Procedures, Male, Multivariate Analysis, Plant Leaves/*chemistry, Proteome/*analysis, Rats, Urine/chemistry
National Category
Chemical Sciences
Identifiers
urn:nbn:se:umu:diva-11772 (URN)10.1021/pr0600071 (DOI)16739992 (PubMedID)
Available from: 2007-12-06 Created: 2007-12-06 Last updated: 2017-12-14
2. Reliable Profile Detection in Comparative Metabolomics
Open this publication in new window or tab >>Reliable Profile Detection in Comparative Metabolomics
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2007 (English)In: Omics, ISSN 1536-2310, E-ISSN 1557-8100, Vol. 11, no 2, 209-224 p.Article in journal (Refereed) Published
Abstract [en]

A strategy for processing of metabolomic GC/MS data is presented. By considering the relationship between quantity and quality of detected profiles, representative data suitable for multiple sample comparisons and metabolite identification was generated. Design of experiments (DOE) and multivariate analysis was used to relate the changes in settings of the hierarchical multivariate curve resolution (H-MCR) method to quantitative and qualitative characteristics of the output data. These characteristics included number of resolved profiles, chromatographic quality in terms of reproducibility between analytical replicates, and spectral quality defined by purity and number of spectra containing structural information. The strategy was exemplified in two datasets: one containing 119 common metabolites, 18 of which were varied according to a DOE protocol; and one consisting of rat urine samples from control rats and rats exposed to a liver toxin. It was shown that the performance of the data processing could be optimized to produce metabolite data of high quality that allowed reliable sample comparisons and metabolite identification. This is a general approach applicable to any type of data processing where the important processing parameters are known and relevant output data characteristics can be defined. The results imply that this type of data quality optimization should be carried out as an integral step of data processing to ensure high quality data for further modeling and biological evaluation. Within metabolomics, this degree of optimization will be of high importance to generate models and extract biomarkers or biomarker patterns of biological or clinical relevance.

Place, publisher, year, edition, pages
Mary Ann Liebert, 2007
National Category
Other Medical Sciences
Identifiers
urn:nbn:se:umu:diva-16147 (URN)10.1089/omi.2007.0006 (DOI)
Available from: 2007-09-28 Created: 2007-09-28 Last updated: 2017-12-14Bibliographically approved
3. Processing of mass spectrometry based metabolomics data for large scale screening studies and diagnostics
Open this publication in new window or tab >>Processing of mass spectrometry based metabolomics data for large scale screening studies and diagnostics
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

In mass spectrometry based metabolomics predictive data processing and sample classification based on representative sample subsets makes it possible to screen large sample banks or data sets in an efficient fashion regarding both data quality and processing time. This is a requirement for making use of high sensitivity and complexity metabolite data and to turn the metabolomics field into a competitive omics platform for biological interpretation and diagnostics. Predictive metabolomics by means of hierarchical multivariate curve resolution (H-MCR) followed by orthogonal partial least squares discriminant analysis (OPLS-DA) was used for the processing and classification of gas chromatography/time of flight mass spectrometry (GC/TOFMS) data characterizing human blood serum samples collected in a study of strenuous physical exercise. The efficiency of the predictive processing as a high throughput tool for generating high quality data is clearly proven and stated as a main benefit of the method. Extensive model validation schemes by means of cross validation and external predictions verified the robustness of the extracted systematic patterns in the data. Comparisons regarding the extracted metabolite patterns between models emphasized the reliability of the methodology in a biological information context. Furthermore, the high predictive power concerning longitudinal predictions provided proof for the diagnostic potential of the methodology. Finally, the predictive metabolite pattern was interpreted physiologically as well as verified in the literature, highlighting the biological relevance of the diagnostic pattern. The suggested approach makes it feasible to screen large data or sample sets with retained data quality and interpretation and to do this in a high throughput fashion. The method could be of value for sample bank mining, metabolome-wide association studies, verification of marker patterns and development of diagnostic systems.

National Category
Other Medical Sciences not elsewhere specified
Identifiers
urn:nbn:se:umu:diva-50969 (URN)
Available from: 2012-01-02 Created: 2012-01-02 Last updated: 2013-03-19Bibliographically approved
4. Metabolomic characterization of human prostate cancer bone metastases reveals increased levels of cholesterol
Open this publication in new window or tab >>Metabolomic characterization of human prostate cancer bone metastases reveals increased levels of cholesterol
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2010 (English)In: PLoS One, ISSN eISSN-1932-6203, Vol. 5, no 12, e14175- p.Article in journal (Refereed) Published
Abstract [en]

Background: Metastasis to the bone is one clinically important features of prostate cancer (PCa). Current diagnostic methods cannot predict metastatic PCa at a curable stage of the disease. Identification of metabolic pathways involved in the growth of bone metastases therefore has the potential to improve PCa prognostication as well as therapy.Methodology/Principal Findings: Metabolomics was applied for the study of PCa bone metastases (n = 20) in comparison with corresponding normal bone (n = 14), and furthermore of malignant (n = 13) and benign (n = 17) prostate tissue and corresponding plasma samples obtained from patients with (n = 15) and without (n = 13) diagnosed metastases and from men with benign prostate disease (n = 30). This was done using gas chromatography-mass spectrometry for sample characterization, and chemometric bioinformatics for data analysis. Results were verified in a separate test set including metastatic and normal bone tissue from patients with other cancers (n = 7). Significant differences were found between PCa bone metastases, bone metastases of other cancers, and normal bone. Furthermore, we identified metabolites in primary tumor tissue and in plasma which were significantly associated with metastatic disease. Among the metabolites in PCa bone metastases especially cholesterol was noted. In a test set the mean cholesterol level in PCa bone metastases was 127.30 mg/g as compared to 81.06 and 35.85 mg/g in bone metastases of different origin and normal bone, respectively (P = 0.0002 and 0.001). Immunohistochemical staining of PCa bone metastases showed intense staining of the low density lipoprotein receptor and variable levels of the scavenger receptor class B type 1 and 3-hydroxy-3-methylglutaryl-coenzyme reductase in tumor epithelial cells, indicating possibilities for influx and de novo synthesis of cholesterol.Conclusions/Significance: We have identified metabolites associated with PCa metastasis and specifically identified high levels of cholesterol in PCa bone metastases. Based on our findings and the previous literature, this makes cholesterol a possible therapeutic target for advanced PCa.

Abstract [sv]

Dottertumörer vid prostatacancer kan spåras via kolesterolDottertumörer i skelettet hos patienter med prostatacancer innehåller höga nivåer av kolesterol och olika aminosyror. Att mäta nivåerna är därför en möjlig väg för att spåra misstänkta dottertumörer. Det visar forskare vid Umeå universitet i en ny studie.Ofta är det förekomsten av dottertumörer, metastaser, som avgör allvaret vid en cancersjukdom. För att behandla patienter med avancerad prostatacancer är det därför viktigt att hitta och identifiera metastaser på ett tidigt stadium. Metastaser från prostatacancer finns ofta i skelettets ben.I studien har forskarna analyserat normal prostatavävnad och vävnad från benmetastaser från patienter med prostatacancer och andra cancersjukdomar. Forskarna har på detta sätt försökt hitta entydiga mönster som utmärker benmetastaser hos patienter med prostatacancer.Något oväntat fann forskarna höga halter av kolesterol i metastasvävnad från patienter med prostatacancer, jämfört med de andra undersökta vävnaderna.– Tumörcellerna verkar både kunna bilda kolesterol och ta upp kolesterol från omgivningen, till exempel från blodet. Vi tror att kolesterol används till olika processer som är viktiga för en tumörcell, till exempel för att den ska kunna växa och invadera omkringliggande vävnad, förklarar Pernilla Wikström, forskare vid institutionen för medicinsk biovetenskap.Spridd prostatacancer brukar behandlas genom att man blockerar produktionen eller effekten av det manliga könshormonet testosteron, så kallad kastrationsbehandling.– Kolesterol används möjligen också till att göra könshormon och det motverkar i sådana fall kastrationsbehandling. Vi håller nu på att utreda detta vidare, tillägger hon.Forskarna fann även höga nivåer av andra metaboliter, substanser som förekommer vid ämnesomsättning i kroppens celler och vävnader. Bland annat upptäckte de vissa aminosyror, och intressant nog gick några av dessa även att spåra i blod hos patienter med metastaser.För att analysera metaboliterna har forskarna använt sig av Sverigeledande teknik på SLU:s laboratorium för metabolomik vid kemiskt biologiskt centrum (KBC) som förestås av professor Thomas Moritz. Genom att haltbestämma metaboliter i olika vävnader eller kroppsvätskor hoppas forskarna kunna hitta specifika mönster av metaboliter för flera olika sjukdomar. Dessa metaboliter skulle sedan kunna mätas i blod hos patienter som till exempel har misstänkta metastaser.– Min forskargrupp har länge arbetat med att utveckla metoder för att använda metabolomik inom medicinska frågeställningar. Målet är att öka möjligheterna att diagnostisera, förstå och i förlängningen behandla allvarliga sjukdomar, säger Henrik Antti, forskare vid kemiska institutionen och handledare till doktoranden Elin Thysell, som genomfört analyserna.Pernilla Wikström och Henrik Antti, som ansvarat för studien, är överens om att de lyckats väl tack vare ett bra samarbete mellan forskare vid prekliniska och kliniska institutioner. Studien har involverat forskare vid kemiska institutionen och Computational Life Science Cluster (CliC), institutionen för medicinisk biovetenskap, Umeå Plant Science Centre (UPSC), samt kliniska forskare inom patologi, onkologi, ortopedi och urologi vid Umeå universitet. Det faktum att man i Umeå har tillgång till en av få biobanker i världen innehållande benmetastaser från patienter var också avgörande för studiens genomförande.

Identifiers
urn:nbn:se:umu:diva-38404 (URN)10.1371/journal.pone.0014175 (DOI)000284939600001 ()
Available from: 2010-12-13 Created: 2010-12-13 Last updated: 2012-01-04Bibliographically approved
5. Evaluation of metabolic alterations in patient plasma associated with disease aggressiveness in prostate cancer
Open this publication in new window or tab >>Evaluation of metabolic alterations in patient plasma associated with disease aggressiveness in prostate cancer
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

GC-MS was used for the study of plasma metabolite profiles in prostate cancer patients. Multivariate analysis of the acquired data revealed metabolites and metabolite patterns associated with prostate cancer disease progression from benign disease to distant metastases. Moreover, by evaluation of plasma metabolite patterns before and after radical prostatectomy differences associated with biochemical relapse was detected. Specifically we found two unidentified plasma metabolites which showed decreased plasma levels with increased disease progression and, furthermore, increased plasma levels post compared to pre surgery in patients who later experienced biochemical relapse. We hypothesize that those metabolites are consumed by aggressive tumors more than by indolent tumors. Identification of those metabolites are hence crucial, and under-way, in order to enable biological interpretation of the results. We further hypothesized that any tumor-derived metabolite secreted into plasma would show increased concentrations with increased PCa risk. Notably we did not detect any such metabolite, but only a few metabolites which showed increased plasma concentrations in patients with metastases compared to patients with benign disease and low risk PCa. In addition, verification of metabolite markers for metastatic disease detected previously by us and others was made, and included decreased plasma levels of stearic acid and increased levels of pseudouridine with metastatic disease.

National Category
Other Medical Sciences not elsewhere specified
Identifiers
urn:nbn:se:umu:diva-50972 (URN)
Available from: 2012-01-02 Created: 2012-01-02 Last updated: 2012-01-04Bibliographically approved

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